Mary E. Martin

Is Nitrogen Deposition Altering the Nitrogen Status of Northeastern Forests

Abstract Concern is resurfacing in the United States over the long-term effects of excess nitrogen (N) deposition and mobility in the environment. We present here a new synthesis of existing data sets for the northeastern United States, intended to answer a single question: Is N deposition altering the N status of forest ecosystems in this region? Surface water data suggest a significant increase in nitrate losses with N deposition. Soil data show an increase in nitrification with decreasing ratio of soil carbon to nitrogen (C:N) but weaker relationships between N deposition and soil C:N ratio or nitrification. Relationships between foliar chemistry and N deposition are no stronger than with gradients of climate and elevation. The differences in patterns for these three groups of indicators are explained by the degree of spatial and temporal integration represented by each sample type. The surface water data integrate more effectively over space than the foliar or soil data and therefore allow a more comprehensive view of N saturation. We conclude from these data that N deposition is altering N status in northeastern forests.

HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES

Remote sensing of foliar chemistry has been recognized as an important element in producing large-scale, spatially explicit estimates of forest ecosystem function. This study was designed to determine whether data from NASAs Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) could be used to determine forest canopy chemistry at a spatial resolution of 20 m, and if so, to use that information to drive an ecosystem pro- ductivity model. Foliage and leaf litter were sampled on 40 plots at Blackhawk Island, Wisconsin, and Harvard Forest, Massachusetts, to determine canopy-level nitrogen and lignin concentrations. At the time of the field sampling, AVIRIS data were acquired for both study areas. Calibration equations were developed, relating nitrogen and lignin to selected first-difference spectral bands (R2 = 0.87 and 0.77, respectively). Calibration equa- tions were evaluated on the basis of inter- and intrasite statistics. These equations were applied to all image pixels to make spatially explicit estimates of canopy nitrogen and lignin for both study sites. These estimates of nitrogen and lignin concentrations were then used with existing models to predict net ecosystem productivity at Harvard Forest and nitrogen mineralization rates at Blackhawk Island.

REGIONAL VARIATION IN FOLIAR CHEMISTRY AND N CYCLING AMONG FORESTS OF DIVERSE HISTORY AND COMPOSITION

Although understanding of nitrogen cycling and nitrification in forest ecosystems has improved greatly over the past several decades, our ability to characterize spatial patterns is still quite limited. A number of studies have shown linkages between canopy chemistry and N cycling, but few have considered the degree to which these trends can provide an indicator of forest N status across large, heterogeneous landscapes. In this study, we examined relationships among canopy chemistry, nitrogen cycling, and soil carbon:nitrogen ratios across 30 forested stands in the White Mountains of New Hampshire. Plots included a range of species (sugar maple, red maple, American beech, yellow birch, paper birch, red spruce, balsam fir, eastern hemlock) and were broadly grouped into two disturbance categories: those that were historically affected by intensive logging and/or fire and those that experienced minimal human disturbance. Across all plots, rates of net N mineralization and net nitrification were correlated wi...

Determining Forest species composition using high spectral resolution remote sensing data

Airborne hyperspectral data were analyzed for the classification of 11 forest cover types, including pure and mixed stands of deciduous and conifer species. Selected bands from first difference reflectance spectra were used to determine cover type at the Harvard Forest using a maximum likelihood algorithm assigning all pixels in the image into one of the 11 categories. This approach combines species specific chemical characteristics and previously derived relationships between hyperspectral data and foliar chemistry. Field data utilized for validation of the classification included both a stand-level survey of stem diameter, and field measurements of plot level foliar biomass. A random selection of validation pixels yielded an overall classification accuracy of 75%.

Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earths climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO2 uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO2 uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle–climate models.

DIRECT ESTIMATION OF ABOVEGROUND FOREST PRODUCTIVITY THROUGH HYPERSPECTRAL REMOTE SENSING OF CANOPY NITROGEN

The concentration of nitrogen in foliage has been related to rates of net photosynthesis across a wide range of plant species and functional groups and thus rep- resents a simple and biologically meaningful link between terrestrial cycles of carbon and nitrogen. Although foliar N is used by ecosystem models to predict rates of leaf-level photosynthesis, it has rarely been examined as a direct scalar to stand-level carbon gain. Establishment of such relationships would greatly simplify the nature of forest C and N linkages, enhancing our ability to derive estimates of forest productivity at landscape to regional scales. Here, we report on a highly predictive relationship between whole-canopy nitrogen concentration and aboveground forest productivity in diverse forested stands of varying age and species composition across the 360 000-ha White Mountain National Forest, New Hampshire, USA. We also demonstrate that hyperspectral remote sensing can be used to estimate foliar N concentration, and hence forest production across a large number of contiguous images. Together these data suggest that canopy-level N concentration is an important correlate of productivity in these forested systems, and that imaging spectrometry of canopy N can provide direct estimates of forest productivity across large landscapes.

Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor

Field studies among diverse biomes demonstrate that mass-based nitrogen concentration at leaf and canopy scales is strongly related to carbon uptake and cycling. Combined field and airborne imaging spectrometry studies demonstrate the capacity for accurate empirical estimation of forest canopy N concentration and other biochemical constituents at scales from forest stands to small landscapes. In this paper, we report on the utility of the first space-based imaging spectrometer, Hyperion, for estimation of temperate forest canopy N concentration as compared to that achieved with the airborne high-altitude imaging spectrometer, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Overall accuracy of Hyperion estimates of forest canopy N concentration, as compared with field measurements, were within 0.25% dry mass, and AVIRIS-based estimates were within 0.19% dry mass, each well within the accuracy required to distinguish among forested ecosystems in nitrogen status.

Chronic nitrogen additions suppress decomposition and sequester soil carbon in temperate forests

The terrestrial biosphere sequesters up to a third of annual anthropogenic carbon dioxide emissions, offsetting a substantial portion of greenhouse gas forcing of the climate system. Although a number of factors are responsible for this terrestrial carbon sink, atmospheric nitrogen deposition contributes by enhancing tree productivity and promoting carbon storage in tree biomass. Forest soils also represent an important, but understudied carbon sink. Here, we examine the contribution of trees versus soil to total ecosystem carbon storage in a temperate forest and investigate the mechanisms by which soils accumulate carbon in response to two decades of elevated nitrogen inputs. We find that nitrogen-induced soil carbon accumulation is of equal or greater magnitude to carbon stored in trees, with the degree of response being dependent on stand type (hardwood versus pine) and level of N addition. Nitrogen enrichment resulted in a shift in organic matter chemistry and the microbial community such that unfertilized soils had a higher relative abundance of fungi and lipid, phenolic, and N-bearing compounds; whereas, N-amended plots were associated with reduced fungal biomass and activity and higher rates of lignin accumulation. We conclude that soil carbon accumulation in response to N enrichment was largely due to a suppression of organic matter decomposition rather than enhanced carbon inputs to soil via litter fall and root production.

Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data

Hyperspectral remote sensing methods are advancing rapidly and offer the promise of estimation of pigment, biochemical, and water content dynamics. The recent Earth Observer 1 (EO-1) Hyperion mission, and associated field campaigns, has allowed a range of biophysical and biochemistry attributes of eucalypt foliage to be analyzed in conjunction with remotely sensed spectra. This paper reports on a study at Tumbarumba (Bago-Maragle State Forest), Australia, which has a wide variety of eucalypt species, ranging in productivity and age. EO-1 Hyperion imagery was obtained in April 2001, and a field program was undertaken involving the establishment of plots, collection of standard forestry inventory data, and green leaf samples. Leaf nitrogen (N) content was measured from leaf samples using wet chemistry techniques and canopy N concentration estimated using leaf mass and proportional species leaf area index data. A number of models were developed from Hyperion reflectance, absorbance, and derivate transformations using partial least squares regression and multiple linear regression. The most significant calibration model predicted N with a correlation coefficient (r)=0.9 (82% variance explained) and a validation r/sup 2/=0.62 (P<0.01). The standard error of the estimate of foliar N was 0.16% equating to 13% of the mean observed %N at the site. These initial results indicate that predictions of canopy foliar N using Hyperion spectra is possible for native multispecies eucalypt forest. Similar studies worldwide, particular those associated with the flux tower network, will allow these findings to be placed in context with other biomes and functional types.

Estimating species abundance in a northern temperate forest using spectral mixture analysis

Effective, reliable methods for characterizing the spatial distribution of tree species through remote sensing would represent an important step toward better understanding changes in biodiversity, habitat quality, climate, and nutrient cycling. Towards this end, we explore the feasibility of using spectral mixture analysis to discriminate the distribution and abundance of two important forest species at the Bartlett Experimental Forest, New Hampshire. Using hyperspectral image data and simulated broadband sensor data, we used spectral unmixing to quantify the abundance of sugar maple and American beech, as opposed to the more conventional approach of detecting presence or absence of discrete species classes. Stronger linear relationships were demonstrated between predicted and measured abundance for hyperspectral than broadband sensor data: R 2 � 0.49 (RMSE � 0.09) versus R 2 � 0.16 (RMSE � 0.19) for sugar maple; R 2 � 0.36 (RMSE � 0.18) versus R 2 � 0.24 (RMSE � 0.33) for beech. These results suggest that spectrally unmixing hyperspectral data to estimate species abundances holds promise for a variety of ecological studies.